Exponential Smoothing

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Presentation transcript:

Exponential Smoothing TIME SERIES MODELS – Exponential Smoothing and Seasonal Indices

Exponential Smoothing Methods This method provides an exponentially weighted moving average of all previously observed values. The aim is to estimate the current level and use it as a forecast of future value. This is an obvious extension the moving average method. With simple moving average forecasts the mean of the past k observations used as a forecast have equal eights (1/k) for all k data points. With exponential smoothing the idea is that the most recent observations will usually provide the best guide as to the future, so we want a weighting scheme that has decreasing weights as the observations get older

Simple Exponential Smoothing Method Formally, the exponential smoothing equation is forecast for the next period.  = smoothing constant. yt = observed value of series in period t. = old forecast for period t. The forecast Ft+1 is based on weighting the most recent observation yt with a weight  and weighting the most recent forecast Ft with a weight of 1- 

Simple Exponential Smoothing Method The implication of exponential smoothing can be better seen if the previous equation is expanded by replacing Ft with its components as follows:

Simple Exponential Smoothing Method If this substitution process is repeated by replacing Ft-1 by its components, Ft-2 by its components, and so on the result is: Therefore, Ft+1 is the weighted moving average of all past observations.

Simple Exponential Smoothing Method The exponential smoothing equation rewritten in the following form elucidate the role of weighting factor . Exponential smoothing forecast is the old forecast plus an adjustment for the error that occurred in the last forecast.

Simple Exponential Smoothing Method The value of smoothing constant  must be between 0 and 1 If stable predictions with smoothed random variation is desired then a small value of  is desire. If a rapid response to a real change in the pattern of observations is desired, a large value of  is appropriate.

Seasonal Analysis Seasonal variation may occur within a year or within a shorter period (month, week) To measure the seasonal effects we construct seasonal indices. Seasonal indexes express the degree to which the seasons differ from the average time series value across all seasons.

Computing Seasonal Indices Remove the effects of the seasonal and random variations by regression analysis: = b0 + b1t For each time period compute the ratio yt/yt which removes most of the trend variation > This is based on the Multiplicative Model. For each season calculate the average of yt/yt which provides the measure of seasonality. Adjust the average above so that the sum of averages of all seasons is equal to number of seasons (Correcting the Seasonal Indices) >

Deseasonalized Time Series Seasonally adjusted time series = Actual time series Seasonal index By removing the seasonality, we can identify changes in the other components of the time series, that might have occurred over time.